An Axiological Matrix for Brazilian Portuguese Bluesky Discourse on AI in Education
Resumo
Public discussion of AI in education is poorly captured by topical retrieval or generic sentiment polarity. We present an axiological matrix, annotation protocol, Brazilian Portuguese Bluesky corpus, classifiers, and temporal projection. The framework filters applicability and maps applicable posts by acceptance and evaluative intensity. Of 1,497 posts, 734 were adjudicated as applicable; pre-adjudication agreement reached 88.4% and Cohen’s κ = .770. Repeated cross-validation shows BERTimbau strongest for applicability (macro-F1 = .903) and three-class intensity, while sparse models remain competitive for acceptance and strongest on five-class tasks. Linear and hybrid pipelines project 989 posts from 2024 into complementary 3×3 and 5×5 matrices.
Referências
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Kleppmann, M., Frazee, P., Gold, J., Graber, S., Jenson, S., Kamber, D., Little, D., Newbold, N., Proctor-Rogers, J., and Ripa, E. (2024). Bluesky and the AT protocol: Usable decentralized social media. In Proceedings of the ACM on Human Factors in Computing Systems (CHI).
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Martin, J. R. and White, P. R. R. (2005). The Language of Evaluation: Appraisal in English. Palgrave Macmillan, London.
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Owoputi, O., O’Connor, B., Dyer, C., Gimpel, K., Schneider, N., and Smith, N. A. (2013). Improved part-of-speech tagging for online conversational text with word clusters. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 380–390. Association for Computational Linguistics.
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Passonneau, R. J. and Carpenter, B. (2014). The benefits of a model of annotation. In Transactions of the Association for Computational Linguistics, volume 2, pages 311–326.
Rosenthal, S., Farra, N., and Nakov, P. (2017). SemEval-2017 task 4: Sentiment analysis in Twitter. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 502–518. Association for Computational Linguistics.
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Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: Pretrained BERT models for Brazilian Portuguese. In Intelligent Systems: 9th Brazilian Conference, BRACIS 2020, pages 403–417. Springer.
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., Agyemang, B., Adzraku, M., Ayanwale, M. A., and Kottahachchi, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1):15.
UNESCO (2023). Guidance for generative AI in education and research. Technical report, United Nations Educational, Scientific and Cultural Organization. Official institutional guidance document.
Voloshinov, V. N. (1973). Marxism and the Philosophy of Language. Seminar Press, New York.
Bakhtin, M. M. (1986). Speech Genres and Other Late Essays. University of Texas Press, Austin.
Barbosa, M. Y. A. (2025). Inteligência artificial e educação: Uma análise de interações discursivas na rede social x. Undergraduate thesis, Universidade Federal de Campina Grande, Campina Grande, Brazil. In Portuguese.
Chan, C. K. Y. and Hu, W. (2023). Students’ voices on generative AI: perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20(1):43.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1):37–46.
Davidson, B. I., Wischerath, D., Racek, D., Parry, D. A., Godwin, E., Hinds, J., van der Linden, D., Roscoe, J. F., Ayravainen, L., and Cork, A. G. (2023). Platform-controlled social media APIs threaten open science. Nature Human Behaviour, 7(12):2054–2057.
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186. Association for Computational Linguistics.
Eisenstein, J. (2013). What to do about bad language on the internet. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 359–369.
He, H. and Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9):1263–1284.
Holmes, W., Bialik, M., and Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign, Boston, MA.
Joachims, T. (1998). Text categorization with Support Vector Machines: Learning with many relevant features. In Proceedings of the 10th European Conference on Machine Learning (ECML 1998), pages 137–142. Springer.
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023). ChatGPT for good? on opportunities and challenges of large language models for education. Learning and Individual Differences, 103:102274.
Kleppmann, M., Frazee, P., Gold, J., Graber, S., Jenson, S., Kamber, D., Little, D., Newbold, N., Proctor-Rogers, J., and Ripa, E. (2024). Bluesky and the AT protocol: Usable decentralized social media. In Proceedings of the ACM on Human Factors in Computing Systems (CHI).
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI), pages 1137–1145. Morgan Kaufmann.
Küçük, D. and Can, F. (2021). Stance detection: A survey. ACM Computing Surveys, 53(1):1–37.
Manning, C. D., Raghavan, P., and Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.
Martin, J. R. and White, P. R. R. (2005). The Language of Evaluation: Appraisal in English. Palgrave Macmillan, London.
Mohammad, S. M., Kiritchenko, S., Sobhani, P., Zhu, X., and Cherry, C. (2016). SemEval-2016 task 6: Detecting stance in tweets. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 31–41. Association for Computational Linguistics.
Owoputi, O., O’Connor, B., Dyer, C., Gimpel, K., Schneider, N., and Smith, N. A. (2013). Improved part-of-speech tagging for online conversational text with word clusters. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 380–390. Association for Computational Linguistics.
Pang, B. and Lee, L. (2008). Opinion Mining and Sentiment Analysis, volume 2 of Foundations and Trends in Information Retrieval. Now Publishers.
Passonneau, R. J. and Carpenter, B. (2014). The benefits of a model of annotation. In Transactions of the Association for Computational Linguistics, volume 2, pages 311–326.
Rosenthal, S., Farra, N., and Nakov, P. (2017). SemEval-2017 task 4: Sentiment analysis in Twitter. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 502–518. Association for Computational Linguistics.
Salton, G. and Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5):513–523.
Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: Pretrained BERT models for Brazilian Portuguese. In Intelligent Systems: 9th Brazilian Conference, BRACIS 2020, pages 403–417. Springer.
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., Agyemang, B., Adzraku, M., Ayanwale, M. A., and Kottahachchi, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1):15.
UNESCO (2023). Guidance for generative AI in education and research. Technical report, United Nations Educational, Scientific and Cultural Organization. Official institutional guidance document.
Voloshinov, V. N. (1973). Marxism and the Philosophy of Language. Seminar Press, New York.
Publicado
19/10/2026
Como Citar
BARBOSA, Marcela Y. A.; BARBOSA, Bryan K. S..
An Axiological Matrix for Brazilian Portuguese Bluesky Discourse on AI in Education. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 23. , 2026, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 469-480.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2026.30029.
